Abstract

Within the framework of the joint Russian-German research project "Volga-Rhine", a continuation of the scientific-technical cooperation started with the "Oka-Elbe"-project in 1992, investigations of the inorganic (this report) and organic pollution (conducted by the working group of Prof. H.F. Schöler) of the Volga rivers aquatic sediments were carried out. Starting with the work of Züllig (1956) sediments have been recognized and used extensively as an almost ideal compartment for the description of the current condition of an aquatic system. Acting as a buffer (sink and source) with respect to heavy metals, nutrients, and major groups of organic pollutants, especially fine grained sediments (< 20µm) are appropriate to reflect the manifold factors determining the status of a water body - at the same time avoiding the time and effort needed to describe these (highly) fluctuating systems by the use of water analysis. Originally intended as a geochemical description of the Volga system, with respect to (heavy) metals and phosphorous, already a cursory error estimation regarding the entire measurement process (chemical analysis as well as sampling itself) changed the intention of this work drastically. Applying the widely accepted guidelines to the expression of uncertainty in measurement (GUM, 1993), as well as adopting basic principles of Gy's Theory of Sampling (Gy, 1998) eventually disproved the validity of numerous geo-chemical approaches towards an interpretation of - not only - sediment data sets, mainly due to shortcomings in the sampling-scheme and thus lacking proof of repre-sentativeness. Although, 'there is an understandable lack of enthusiasm for rousing the sleeping dogs of sampling when there is a fair chance of being severely bitten' (Thompson, 1999) it is the only way that will lead to justifiable interpretations and decisions within the framework of any sampling scheme. Making a sample demonstrably representative for anything but itself, inevitably leads to uncertainty-budgets typically in the range of 20%-100%. This in fact leaves little more opportunities than: a) return to robust classification systems (like the Igeo-classes (Müller, 1979)), b) group multiple samples in order to achieve the desired precision (this report), or c) change the methodology (e.g. the US-EPA TRIAD-approach (Crumbling, 2001)).